Forgery facial images and videos have increased the concern of digital security. It leads to the significant development of detecting forgery data recently. However, the data, especially the videos published on the Internet, are usually compressed with lossy compression algorithms such as H.264. The compressed data could significantly degrade the performance of recent detection algorithms. The existing anti-compression algorithms focus on enhancing the performance in detecting heavily compressed data but less consider the compression adaption to the data from various compression levels. We believe creating a forgery detection model that can handle the data compressed with unknown levels is important. To enhance the performance for such models, we consider the weak compressed and strong compressed data as two views of the original data and they should have similar representation and relationships with other samples. We propose a novel anti-compression forgery detection framework by maintaining closer relations within data under different compression levels. Specifically, the algorithm measures the pair-wise similarity within data as the relations, and forcing the relations of weak and strong compressed data close to each other, thus improving the discriminate power for detecting strong compressed data. To achieve a better strong compressed data relation guided by the less compressed one, we apply video level contrastive learning for weak compressed data, which forces the model to produce similar representations within the same video and far from the negative samples. The experiment results show that the proposed algorithm could boost performance for strong compressed data while improving the accuracy rate when detecting the clean data.

翻译：伪造人脸图像与视频加剧了数字安全隐患，推动了近期伪造数据检测技术的显著发展。然而，互联网上发布的视频数据通常采用H.264等有损压缩算法处理，压缩后的数据会显著降低现有检测算法的性能。现有抗压缩算法主要致力于提升对重度压缩数据的检测能力，但较少考虑模型对不同压缩级别数据的自适应能力。我们认为构建能够处理未知压缩级别数据的伪造检测模型至关重要。为提升此类模型的性能，本文将弱压缩与强压缩数据视为原始数据的两种视图，二者应具备相似的表示特征及与其他样本的关系模式。我们提出一种新型抗压缩伪造检测框架，通过保持不同压缩级别数据间更紧密的关系来实现目标。具体而言，该算法通过度量数据间的成对相似性构建关系矩阵，并强制弱压缩与强压缩数据的关系分布相互逼近，从而增强对强压缩数据的判别能力。为实现弱压缩数据对强压缩数据关系更优的引导，我们对弱压缩数据应用视频级对比学习，迫使模型对同一视频产生相似表征，同时拉远与负样本的距离。实验结果表明，所提算法在提升强压缩数据检测性能的同时，亦能提高对干净数据的检测准确率。